• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.11a
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• pp.241-250
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• 2005

New product development (NPD) is defined as the transformation of a market opportunity and a set of assumptions about product technology into a product available for sale. Managers charged with project selection decisions in the NPD process, such as go/no-go choices and specific resource allocation decisions, are faced with a complicated problem. Therefore, the ability to develop new successful products has identifies as a major determinant in sustaining a firm's competitive advantage. The purpose of this study is to develop a new evaluation model for NPD project selection in the high -tech industry using support vector machines (SYM). The evaluation model is developed through two phases. In the first phase, binary (go/no-go) classification prediction model, i.e. SVM for high-tech NPD project selection is developed. In the second phase. using the predicted output value of SVM, feasibility grade is calculated for the final NPD project decision making. In this study, the feasibility grades are also divided as three level grades. We assume that the frequency of NPD project cases is symmetrically determined according to the feasibility grades and misclassification errors are partially minimized by the multiple grades. However, the horizon of grade level can be changed by firms' NPD strategy. Our proposed feasibility grade method is more reasonable in NPD decision problems by considering particularly risk factor of NPD in viewpoints of future NPD success probability. In our empirical study using Korean NPD cases, the SVM significantly outperformed ANN and logistic regression as benchmark models in hit ratio. And the feasibility grades generated from the predicted output value of SVM showed that they can offer a useful guideline for NPD project selection.

• Journal of Korean Biological Nursing Science
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• v.23 no.3
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• pp.217-226
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• 2021

Purpose: The aim of this study was to investigate the risk factors for brain reperfusion injury in ischemic stroke patients and to analyze the clinical outcomes. Methods: A retrospective study was conducted in 168 patients who underwent mechanical thrombectomy. The data were analyzed using descriptive statistics, t-test, Mann-Whitney U test, Chi-Square test, Fisher's exact test, and logistic regression with IBM SPSS/WIN 24.0. Results: Brain reperfusion injury occurred in 67 patients (39.9%) with a low favored outcome (𝛘²=6.01, p=.014). On multivariable analysis, blood urea nitrogen (Odds ratio [OR]=1.14, 95% Confidence interval [CI]=1.06-1.23), aphasia (OR=6.16, CI=1.62-23.40), anosognosia (OR=4.84, CI=1.13-20.79), presence of both aphasia and anosognosia (OR=7.33, CI=1.20-44.60), and time required to achieve targeted blood pressure (OR=1.00, CI=1.00-1.00) were identified as risk factors for brain reperfusion injury. A statistically significant difference was detected in clinical outcomes, including hemorrhagic transformation (𝛘²=6.32, p=.012), intensive care unit length of stay (Z=-2.08, p=.038), National Institute of Health Stroke scale score at discharge (Z=-3.14, p=.002), and modified Rankin Scale score at discharge (Z=-2.93, p=.003). Conclusion: This study identified the risk factors and presented the clinical outcomes of brain reperfusion injury. It is necessary to consider these risk factors for evaluating the patients and to establish nursing interventions and strategies.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.4
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• pp.186-191
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• 2023

Data processing techniques play a critical role in decision-making, including handling missing and outlier data, prediction, and recommendation models. This requires a clear explanation of the validity, reliability, and accuracy of all processes and results. In addition, it is necessary to solve data problems through explainable models using decision trees, inference, etc., and proceed with model lightweight by considering various types of learning. The multi-layer mining classification method that applies the sixth principle is a method that discovers multidimensional relationships between variables and attributes that occur frequently in transactions after data preprocessing. This explains how to discover significant relationships using mining on transactions and model the data through regression analysis. It develops scalable models and logistic regression models and proposes mining techniques to generate class labels through data cleansing, relevance analysis, data transformation, and data augmentation to make explanatory decisions.

• Journal of the Korean Society of School Health
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• v.36 no.3
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• pp.103-112
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• 2023

Purpose: The COVID-19 pandemic, which emerged in late 2019, had a profound impact on global public health and disrupted the daily lives of people worldwide. Particularly, university students faced a challenging situation as their university life underwent a drastic transformation due to long-term remote learning and isolation measures. This study aimed to investigate the relationship between changes in daily life during the 2020 COVID-19 pandemic and depressive symptoms among university students aged between 19 and 29 in Korea. Methods: We analyzed data from the nationally representative 2020 Community Health Survey (CHS). Among the 229,269 participants, 9,279 university students aged 19-29, either enrolled or on leave, were selected. After excluding 401 cases with missing values, the final sample comprised 8,878 individuals. Using multivariate logistic regression with a complex sample design, we explored the association between daily life changes during the COVID-19 pandemic and depressive symptoms. Results: Changes in daily life during the COVID-19 pandemic was associated with depressive symptoms in Korean university students aged 19 to 29, even after adjusting for sociodemographic characteristics, health-related factors, and COVID-19-related aspects (OR=1.28, 95% CI=1.09~1.50). Conclusion: Our study suggests that when examining the impact of COVID-19 on health issues, it is crucial to consider the changes in daily life caused by the pandemic. These findings can provide insights into the psychological well-being of university students during times of crisis.

• The Journal of Internal Korean Medicine
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• v.30 no.3
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• pp.510-524
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• 2009

Objective : This study was designed to find the correlation between Sasang Constitution and heart rate variability(HRV). Method : There were 665 subjects (280 men and 385 women), between 39 and 72 years old. in a rural community. Sasang Constitution was diagnosed by a Sasang constitutional specialist using PSSC (Phonetic System for Sasang Constitution), face and tongue photo and checkup-list. A structured-questionnaire was used to assess the general characteristics. HRV was recorded using SA-2000 (medi-core). HRV was assessed by time domain and by frequency domain analysis. Metabolic syndrome was defined on the basis of clustering of risk factors, when three or more of the following cardiovascular risk factors were included : blood pressure, fasting blood sugar, triglyceride HDL-cholesterol, and abdominal obesity (waist). Because of the skewness of the data, logarithmic transformation was performed on the absolute units of the spectral components of HRV, and the resulting logarithmic values and normalized units were compared between the groups by a logistic regression. The 95% confidence interval (CI) of the odds ratio was used and calculated from the data laid out for a cross sectional study. Results : 1. Odds ratios of Taeeumin and Soeumin in female adults below 60 years old were significantly lower than that of Soyangin in LF norm and LF/HF ratio. Odds ratios of Taeeumin and Soeumin in female adults below 60 years old were significantly higher than that of Soyangin in HF norm. 2. There was no significant correlation between HRV and Sasang Constitution in female adults from 60 years old and over. 3. There was no significant correlation between HRV and Sasang Constitution in male adults. Conclusion : There is a statistically significant correlation between the HRV and Sasang Constitution. There is a tendency of increase in the sympathetic activity in Soyangin. There is a tendency of decrease in the parasympathetic activity in Taeeumin and Soeumin.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.8
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• pp.4175-4179
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• 2016

Background: Oral cavity cancer (OCC) is one of the most common cancers worldwide. No studies have reported on the prevalence and epidemiologic risk factors of oral premalignant lesions (OPLs) in Thailand. The purpose of this study was to investigate the prevalence of OPLs and associated factors in Roi Et Province, Thailand. Materials and Methods: To investigate the prevalence of OPLs, a cross-sectional descriptive study was conducted in which 2,300 subjects over 40 years of age were recruited and screened for the prevalence of OPLs. To identify factors associated with OPLs, a matched case-control study was used in which the subjects were 102 cases with OPL and 102 matched controls without OPLs. The studies were conducted in Roi Et Province during the period 1 February, 2014, to 30 April, 2014, and the data were collected by the use of a structured interview questionnaire and by extraction of information from medical records. Data analyses involved the use of descriptive statistics, McNemar's test, and conditional logistic regression. Results: The overall prevalence of OPLs was 3.8%, and no-one was diagnosed with more than one type of OPL. The factors found to be associated with a statistically significant higher risk of an OPL were betel nut chewing, smoking, and alcohol consumption. The associations with these factors were strong, especially for betel nut chewing and smoking. Conclusions: The habits of betel nut chewing, smoking, and alcohol use are confirmed as factors associated with OPLs in a population of Roi Et Province, Thailand. Campaigns to reduce such risk healthy behaviour are needed, but whether any actual decrease will prevent the eventual transformation of an OPL into an OCC remains an open question.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.10
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• pp.4215-4218
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• 2015

Gastric cancer (GC) is one of the most common malignancies in the world. It is the first cause of cancer deaths in both sexes In Iranian population. Circulating insulin-like growth factor-one (IGF-1) levels have been associated for gastric cancer. IGF-1 protein has central roles involved in the regulation of epithelial cell growth, proliferation, transformation, apoptosis and metastasis. Single nucleotide polymorphism in IGF-1 regulatory elements may lead to alter in IGF-1expression level and GC susceptibility. The aim of this study was to investigate the influence of IGF-1 gene polymorphism (rs5742612) on risk of GC and clinicopathological features for the first time in Iranian population. In total, 241 subjects including 100 patients with GC and 141 healthy controls were recruited in our study. Genotypes were analyzed using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) assay with DNA from peripheral blood. The polymorphism was statistically analyzed to investigate the relationship with the risk of GC and clinicopathological properties. Logistic regression analysis revealed that there was no significant association between rs5742612 and the risk of GC. In addition, no significant association between genotypes and clinicopathological features was observed (p value>0.05). The frequencies of the CC, CT, and TT genotypes were 97%, 3%, and 0%, respectively, among the cases, and 97.9%, 2.1%, and 0%, respectively, among the controls. CC genotype was more frequent in cases and controls. The frequencies of C and T alleles were 98.9% and 1.1% in controls and 98.5% and 1.5% in patient respectively. Our results provide the first evidence that this variant is rare in Iranian population and it may not be a powerful genetic predisposing biomarker for prediction GC clinicopathological features in an Iranian population.